Background: Breast cancer diagnostic data is complex and accompanied by noise. Quantum machine learning can enhance the accuracy, efficiency, and scalability of artificial intelligence algorithms and has applications in various fields such as drug discovery and personalized medicine. Methods: In the systematic review conducted, the databases PubMed, Embase, Scopus, and Web of Science were searched in December 2024. The search strategy included the keywords "Breast Cancer," "Artificial Intelligence," and "Quantum machine learning" along with their synonyms in article titles. Descriptive, qualitative, review, and non-English studies were excluded. The qualitative evaluation of the articles and the assessment of their bias were determined based on the Joanna Briggs Institute (JBI) indicators checklist. Results: Twenty-nine studies utilizing artificial intelligence models for personalized breast cancer management were selected. Seventeen studies employing various deep learning methods achieved satisfactory results in predicting treatment response and prognosis, effectively contributing to the personalized management of breast cancer. Twenty-six studies demonstrated that machine learning methods could enhance the processes of classification, screening, diagnosis, and prognosis of breast cancer. The methods most frequently used in modeling were quantum support vector machine (QSVM), quantum convolutional neural network (QCNN), and quantum neural network (QNN), with an average AUC of 0.91. Additionally, the average accuracy, sensitivity, specificity, and precision indices of the models ranged from 90% to 96%. Conclusion: Quantum computing can address some challenges arising from the increasing complexity and size of artificial intelligence models. Overall, the combination of artificial intelligence and quantum computing can significantly accelerate the drug discovery process and the development of effective drugs.
Background: Breast cancer diagnostic data is complex and accompanied by noise. Quantum machine learning can enhance the accuracy, efficiency, and scalability of artificial intelligence algorithms and has applications in various fields such as drug discovery and personalized medicine. Methods: In the systematic review conducted, the databases PubMed, Embase, Scopus, and Web of Science were searched in December 2024. The search strategy included the keywords "Breast Cancer," "Artificial Intelligence," and "Quantum machine learning" along with their synonyms in article titles. Descriptive, qualitative, review, and non-English studies were excluded. The qualitative evaluation of the articles and the assessment of their bias were determined based on the Joanna Briggs Institute (JBI) indicators checklist. Results: Twenty-nine studies utilizing artificial intelligence models for personalized breast cancer management were selected. Seventeen studies employing various deep learning methods achieved satisfactory results in predicting treatment response and prognosis, effectively contributing to the personalized management of breast cancer. Twenty-six studies demonstrated that machine learning methods could enhance the processes of classification, screening, diagnosis, and prognosis of breast cancer. The methods most frequently used in modeling were quantum support vector machine (QSVM), quantum convolutional neural network (QCNN), and quantum neural network (QNN), with an average AUC of 0.91. Additionally, the average accuracy, sensitivity, specificity, and precision indices of the models ranged from 90% to 96%. Conclusion: Quantum computing can address some challenges arising from the increasing complexity and size of artificial intelligence models. Overall, the combination of artificial intelligence and quantum computing can significantly accelerate the drug discovery process and the development of effective drugs.
Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.
Experts predict that the use of artificial intelligence and quantum computing will transform medicine including medical imaging. One of the common malignant tumors in women and seriously threatens women’s physical and mental health is breast cancer. The high incidence and mortality of breast cancer are seriously threatening women’s physical and mental health. The long time it took to get breast cancer’s test result and the conditions which make delay without being treated which caused the loss of lives due to latency. Early screening for breast cancer via mammography, ultrasound (US) and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. Artificial intelligence has been extensively researched for breast cancer screening and has demonstrated good performance in image identification tests. This research was introduced analytical estimation for quantum Convolutional Neural Network for breast cancer detection, such as in the identification, segmentation and classification of lesions, breast density assessment and breast cancer risk assessment. Objectives will be to: To detect the breast cancer on a quantum convolutional neural network algorithm using image recognition, to create and develop a CNN and QCNN algorithm that will detect breast Cancer using Deep Learning and to early predict pathological complete response to neoadjuvant chemotherapy and survival analysis using Deep Learning. Data was analyzed through the use of descriptive and deep learning models like CNN and QCNN models. This research used also Google Collab to analyze the data and used data analysis tools python as programming language. From the study results, we have demonstrated the efficacy of quantum convolutional neural networks (QCNNs) to detect breast cancer cells. Using the techniques of deep learning and supervised learning in the quantum framework, we have proposed and tested the effectiveness of quantum CNN on the quantum simulator available at the IBM quantum experience platform.
Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation.
Breast cancer continues to be among the most common causes of cancer death globally, with early and precise diagnosis playing a pivotal role in enhancing patient survival. Traditional machine learning (ML) techniques have shown great promise in the field of medical imaging and diagnosis; however, the emergence of quantum machine learning (QML) offers new avenues for the improvement of pattern discovery and diagnostic accuracy in high-dimensional medical data. This work describes a thorough benchmark comparison of quantum and classical machine learning methods for breast cancer diagnosis over the Wisconsin Diagnostic Breast Cancer data and mammographic image data. We compare variational quantum classifiers (VQC), quantum kernel methods (QKM), quantum convolutional neural networks (QCNNs), and hybrid quantum-classical neural structures with traditional classical baselines like support vector machines (SVM) and convolutional neural networks (CNN). QKM techniques perform better in high-dimensional feature spaces and better generalize on external validation sets. This implementation framework describes in depth how to develop, train, and deploy QML models in the clinical workflow, including optimization approaches, code structures, and deployment issues. The work demonstrates that it is possible to streamline breast cancer screening and diagnosis using QML with more accurate and more efficient solutions, which would lead to a huge impact on patient outcomes.
Breast cancer continues to pose a formidable global health challenge, claiming countless lives each year and highlighting the urgent need for early and precise detection. Timely diagnosis is critical for improving treatment outcomes and survival rates, yet conventional screening and diagnostic methods often fall short in handling complex medical data efficiently. Over the past two decades, artificial intelligence (AI) and machine learning (ML) have revolutionized breast cancer detection by analyzing medical imaging, histopathology, and genomic profiles. Despite their success, these classical approaches are constrained by high computational costs, reliance on large annotated datasets, and reduced generalization when data is limited or heterogeneous. Quantum Machine Learning (QML) has emerged as a transformative paradigm, integrating quantum computing principles—such as superposition, entanglement, and quantum parallelism—with classical learning frameworks to overcome these limitations. This survey provides a comprehensive overview of QML applications in breast cancer detection by reviewing key methodologies and advancements. It begins with a discussion of the clinical profile, treatment approaches, and recent epidemiological trends of breast cancer. Subsequently, it examines traditional AI and ML techniques used in diagnostic modeling, followed by emerging QML frameworks that leverage quantum principles for enhanced learning and efficiency. The paper concludes by outlining current challenges in QML research, including hardware constraints, noise sensitivity, and the lack of standardized evaluation benchmarks.
Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide, and early detection plays a crucial role in improving survival rates and treatment outcomes.Conventional diagnostic methods such as mammography, ultrasound imaging, and histopathological analysis rely heavily on expert interpretation and classical machine learning techniques.Although classical deep learning models, especially Convolutional Neural Networks (CNNs), have demonstrated impressive performance in medical image analysis, they face inherent limitations related to computational complexity, scalability, and the curse of dimensionality when handling large, high-resolution datasets.With the rapid advancement of quantum computing, Quantum Machine Learning (QML) has emerged as a promising paradigm capable of addressing these challenges by leveraging quantum phenomena such as superposition and entanglement.This research explores the application of Quantum Convolutional Neural Networks (Quantum CNNs or QCNNs) for breast cancer detection, aiming to enhance classification accuracy while reducing computational overhead.The proposed approach integrates quantum circuits with classical preprocessing techniques to extract discriminative features from breast cancer datasets, such as mammographic or histopathological images.By encoding image features into quantum states and performing convolution operations through parameterized quantum circuits, the Quantum CNN model is designed to learn complex patterns more efficiently than classical counterparts.The study evaluates the effectiveness of Quantum CNNs in distinguishing between benign and malignant breast tumors and compares its performance with traditional CNN models.The findings highlight the potential of Quantum CNNs to revolutionize medical image analysis by offering faster convergence, improved generalization, and robustness against noise.This research contributes to the growing field of quantumassisted healthcare systems and demonstrates how quantum computing can be harnessed to address critical challenges in breast cancer diagnosis.